Submitted

• J. Paisley, C. Wang, D. Blei, and M. Jordan.   Nested hierarchical Dirichlet processes.   October, 2012.   [ArXiv]
• S. Gerrish and D. Blei.   The issue-adjusted ideal point model.   September, 2012.   [ArXiv]
• G. Polatkan, M. Zhou, L. Carin, D. Blei, and I. Daubechies.   A Bayesian nonparametric approach to image super-resolution.   September, 2012.   [ArXiv]
• S. Gershman, P. Frazier, and D. Blei.   Distance dependent infinite latent feature models.   September, 2012.   [ArXiv] [Code]

2013

• M. Hoffman, D. Blei, J. Paisley, and C. Wang.   Stochastic variational inference.   Journal of Machine Learning Research, to appear.   [PDF]
• C. Wang and D. Blei.   Variational inference in nonconjugate models.   Journal of Machine Learning Research, to appear.   [PDF] [Code]
• D. Blei.   Topic Modeling and Digital Humanities.   Journal of Digital Humanities, 2(1), 2013.   [Link]
• R. Ranganath, C. Wang, D. Blei, and E. Xing.  An adaptive learning rate for stochastic variational inference.   International Conference on Machine Learning, 2013. [PDF]

2012

• S. Gerrish and D. Blei.   How they vote: Issue-adjusted models of legislative behavior   Neural Information Processing Systems, 2012.   [PDF]
• P. Gopalan, D. Mimno, S. Gerrish, M. Freedman, and D. Blei.   Scalable inference of overlapping communities.   Neural Information Processing Systems, 2012.   [PDF]
• C. Wang and D. Blei.   Truncation-free stochastic variational inference for Bayesian nonparametric models.   Neural Information Processing Systems, 2012.   [PDF]
• D. Blei.   Probabilistic topic models.   Communications of the ACM, 55(4):77–84, 2012.   [PDF]
• J. Paisley, C. Wang, and D. Blei.   The discrete infinite logistic normal distribution.   Bayesian Analysis, 7(2):235–272, 2012.   [PDF] [C Code] [Matlab]
• J. Paisley, D. Blei, and M. Jordan.   Variational Bayesian inference with stochastic search.   International Conference on Machine Learning, 2012.   [PDF]
• D. Mimno, M. Hoffman, and D. Blei.   Sparse stochastic inference for latent Dirichlet allocation.   International Conference on Machine Learning, 2012.   [PDF] [Code]
• S. Gershman, M. Hoffman, and D. Blei.   Nonparametric variational inference.   International Conference on Machine Learning, 2012.   [PDF] [Code]
• A. Chaney and D. Blei.   Visualizing topic models.   International AAAI Conference on Social Media and Weblogs, 2012.   [PDF]
• J. Paisley, D. Blei, and M. Jordan.   Stick-breaking beta processes and the Poisson process.   Artificial Intelligence and Statistics, 2012.   [PDF]
• S. Gershman and D. Blei.   A tutorial on Bayesian nonparametric models.   Journal of Mathematical Psychology, 56:1–12, 2012.   [PDF]

2011

• S. Ghosh, A. Ungureunu, E. Sudderth, and D. Blei.   Spatial distance dependent Chinese restaurant processes for image segmentation.   Neural Information Processing Systems, 2011.   [PDF]
• D. Blei. and P. Frazier.   Distance dependent Chinese restaurant processes.   Journal of Machine Learning Reseach, 12:2461–2488, 2011.   [PDF] [Code]
• L. Hannah, D. Blei, and W. Powell.   Dirichlet process mixtures of generalized linear models.   Journal of Machine Learning Research, 12:1923–1953.   [PDF]
• C. Wang and D. Blei.   Collaborative topic modeling for recommending scientific articles. Knowledge Discovery and Data Mining, 2011.   (Best Student Paper Award)   [PDF] [Code and Demo]
• D. Mimno and D. Blei.   Bayesian checking of topic models.   Empirical Methods in Natural Language Processing, 2011.   [PDF]
• S. Gershman, D. Blei, F. Pereira, and K. Norman.   A topographic latent source model for fMRI data.   NeuroImage, 57:89–100, 2011.   [PDF]
• J. Paisley, L. Carin, and D. Blei.   Variational inference for stick-breaking beta processes.   International Conference on Machine Learning, 2011.   [PDF]
• S. Gerrish and D. Blei.   Predicting legislative roll calls from text.   International Conference on Machine Learning, 2011.   (Distinguished Application Paper Award)   [PDF]
• J. Paisley, C. Wang, and D. Blei.   The discrete infinite logistic normal distribution for mixed-membership modeling.   Artificial Intelligence and Statistics, 2011.   (Notable Paper Award)   [PDF] [C Code][Matlab]
• C. Wang, J. Paisley, and D. Blei.   Online variational inference for the hierarchical Dirichlet process.   Artificial Intelligence and Statistics , 2011. [PDF] [Code]

2010

• M. Hoffman, D. Blei, and F. Bach.   Online learning for latent Dirichlet allocation   Neural Information Processing Systems, 2010.   [PDF] [Supplement] [Code]
• L. Hannah, W. Powell, and D. Blei.   Nonparametric density estimation for stochastic optimization with an observable state variable   Neural Information Processing Systems, 2010.   [PDF] [Supplement] [Long paper]
• J. Chang and D. Blei.   Hierarchical relational models for document networks.   Annals of Applied Statistics, 4(1):124–150, 2010.   [PDF] [Code]
• D. Blei and P. Frazier.   Distance dependent Chinese restaurant processes.   International Conference on Machine Learning, 2010.   [PDF] [Long paper] [Code]
• S. Gerrish and D. Blei.   A language-based approach to measuring scholarly impact.   International Conference on Machine Learning, 2010.   [PDF]
• M. Hoffman, D. Blei, and P. Cook.   Bayesian nonparametric matrix factorization for recorded music.   International Conference on Machine Learning, 2010.   [PDF]
• S. Williamson, C. Wang, K. Heller, and D. Blei.   The IBP compound Dirichlet process and its application to focused topic modeling.   International Conference on Machine Learning, 2010.   [PDF]
• J. Li, C. Wang, Y. Lim, D. Blei, and L. Fei-Fei.   Building and using a semantivisual image hierarchy.   Computer Vision and Pattern Recognition, 2010.   [PDF]
• S. Cohen, D. Blei, and N. Smith.   Variational inference for adaptor grammars.   North American Chapter of the Association for Computational Linguistics, 2010.   [PDF]
• L. Hannah, D. Blei, and W. Powell.   Dirichlet process mixtures of generalized linear models. Artificial Intelligence and Statistics, 2010.   [PDF]
• A. Lorbert, D. Eis, V. Kostina, D. Blei, and P. Ramadge.   Exploiting covariate similarity in sparse regression via the pairwise elastic net. Artificial Intelligence and Statistics, 2010.   [PDF]
• D. Blei, T. Griffiths, and M. Jordan.   The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies.   Journal of the ACM, 57:2 1–30, 2010.   [PDF] [Code] [JACM abstracts]
• S. Gershman, D. Blei, and Y. Niv.   Context, Learning and Extinction   Psychological Review 117:1 197–209, 2010.   [PDF]

2009

• J. Chang, J. Boyd-Graber, S. Gerrish, C. Wang, and D. Blei.   Reading tea leaves: How humans interpret topic models .   Neural Information Processing Systems, 2009.   [PDF]
• C. Wang and D. Blei.   Decoupling sparsity and smoothness in the discrete hierarchical Dirichlet process.   Neural Information Processing Systems, 2009.   [PDF] [Supplement]
• C. Wang and D. Blei.   Variational inference for the nested Chinese restaurant process. Neural Information Processing Systems, 2009.   [PDF]
• R. Socher, S. Gershman, A. Perotte, P. Sederberg, D. Blei, and K. Norman. A Bayesian analysis of dynamics in free recall. Neural Information Processing Systems, 2009.   [PDF] [Code and data]
• M. Hoffman, D. Blei, P. Cook.   Finding Latent Sources in Recorded Music With a Shift-Invariant HDP.   International Conference on Digital Audio Effects, 2009.   [PDF]
• J. Boyd-Graber and D. Blei.   Multilingual topic models for unaligned text.   Uncertainty in Artificial Intelligence, 2009.   [PDF]
• J. Chang, J. Boyd-Graber, and D. Blei.   Connections between the lines: Augmenting social networks with text.   Knowledge Discovery and Data Mining, 2009.   [PDF] [Code]
• C. Wang, D. Blei., and L. Fei-Fei.   Simultaneous image classification and annotation.   Computer Vision and Pattern Recognition, 2009.   [PDF] [Code]
• M. Hoffman, P. Cook, and D. Blei.   Bayesian spectral matching: Turning Young MC into MC Hammer via MCMC sampling International Computer Music Conference, 2009.   [PDF]
• J. Chang and D. Blei.   Relational Topic Models for Document Networks . Artificial Intelligence and Statistics, 2009.   [PDF] [Long version]
• C. Wang, B. Thiesson, C. Meek, and D. Blei.   Markov topic models.   Artificial Intelligence and Statistics, 2009.   [PDF]
• M. Hoffman, D. Blei, and P. Cook.   Easy as CBA: A simple probabilistic model for tagging music. International Conference on Music Information Retrieval, 2009.   (Best Student Paper Award)   [PDF]
• D. Blei and J. Lafferty.   Topic Models.   In A. Srivastava and M. Sahami, editors, Text Mining: Classification, Clustering, and Applications . Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, 2009.   [PDF]

2008

• E. Airoldi, D. Blei, S. Fienberg, and E. Xing.   Mixed membership stochastic blockmodels.   Journal of Machine Learning Research , 9:1981--2014, 2008 .   [PDF][Code] [Shorter version from NIPS 2008]
• I. Mukherjee and D. Blei.   Relative performance guarantees for approximate inference in latent Dirichlet allocation.   Neural Information Processing Systems, 2008.   [PDF]
• J. Boyd-Graber and D. Blei.   Syntactic topic models.   Neural Information Processing Systems, 2008.   [PDF] [Supplement] [Long version]
• M. Hoffman, D. Blei, and P. Cook. Content-based musical similarity computation using the hierarchical Dirichlet process. In International Conference on Music Information Retrieval, 2008. [PDF]
• M. Hoffman, P. Cook, and D. Blei. Data-driven recomposition using the hierarchical Dirichlet process hidden Markov model. In International Computer Music Conference, 2008. [PDF]
• C. Wang, D. Blei, and D. Heckerman. Continuous time dynamic topic models. In Uncertainty in Artificial Intelligence [UAI], 2008. [PDF]

2007

• D. Blei, J. McAuliffe. Supervised topic models. Neural Information Processing Systems 21, 2007. [PDF] [Long version][digg data] [Code]
• J. Boyd-Graber, D. Blei, and X. Zhu. A topic model for word sense disambiguation. In Empirical Methods in Natural Language Processing, 2007. [PDF]
• W. Li, D. Blei, and A. McCallum. Nonparametric Bayes pachinko allocation. In The 23rd Conference on Uncertainty in Artificial Intelligence, 2007. [PDF]
• D. Kaplan and D. Blei. A computational approach to style in American poetry. In IEEE Conference on Data Mining, 2007.
• D. Blei and J. Lafferty. A correlated topic model of Science. Annals of Applied Statistics. 1:1 17–35, 2007. [PDF] [Code][Browser]
• M. Dudik, D. Blei, and R. Schapire. Hierarchical maximum entropy density estimation. Proceedings of the 24th International Conference on Machine Learning, 2007. [PDF]

2006

• D. Blei and J. Lafferty. Dynamic topic models. In Proceedings of the 23rd International Conference on Machine Learning, 2006. [PDF]
• D. Blei and J. Lafferty. Correlated Topic Models. Neural Information Processing Systems, 2006. [PDF] [Long version][Code]
• J. McAuliffe, D. Blei, and M. Jordan. Nonparametric empirical Bayes for the Dirichlet process mixture model. Statistics and Computing, 16[1]:5–14, 2006. [Springer] [TR PDF]
• D. Blei and M. Jordan. Variational inference for Dirichlet process mixtures. Journal of Bayesian Analysis, 1[1]:121–144, 2006. [A shorter version appeared in ICML 2004]. [PDF]
• Y. Teh, M. Jordan, M. Beal, and D. Blei. Hierarchical Dirichlet processes. Journal of the American Statistical Association, 2006. 101[476]:1566-1581. [PDF] [Teh's Matlab Code] [Chong Wang's C code]

Before 2006

• T. Griffiths, M. Steyvers, D. Blei, and J. Tenenbaum. Integrating topics and syntax. Neural Information Processing Systems 17, 2005. [PDF]
• D. Blei. Probabilistic Models of Text and Images. PhD thesis, U.C. Berkeley, Division of Computer Science, 2004. [PDF]
• D. Blei and M. Jordan. Modeling annotated data. In Proceedings of the 26th annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 127–134. ACM Press, 2003. [PDF]
• D. Blei, T. Griffiths, M. Jordan, and J. Tenenbaum. Hierarchical topic models and the nested Chinese restaurant process. Neural Information Processing Systems 16, 2003. [PDF]
• K. Barnard, P. Duygulu, N. de Freitas, D. Forsyth, D. Blei, and M. Jordan. Matching words and pictures. Journal of Machine Learning Research, 3:1107–1135, 2003. [PDF]
• D. Blei, A. Ng, and M. Jordan. Hierarchical Bayesian models for applications in information retrieval. In J. Bernardo, J. Berger, A. Dawid, D. Heckerman, A. Smith, and M. West, editors, Bayesian Statistics 7, volume 7, pages 25–44. Oxford University Press, 2003.
• D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993–1022, January 2003. [A shorter version appeared in NIPS 2002]. [PDF] [Code]
• D. Blei, J. Bagnell, and A. McCallum. Learning with scope, with application to information extraction and classification. In Uncertainty in Artificial Intelligence: Proceedings of the Eighteenth Conference [UAI-2002], pages 53–60, San Francisco, CA, 2002. Morgan Kaufmann Publishers.
• D. Blei and P. Moreno. Topic segmentation with an aspect hidden Markov model. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pages 343–348. ACM Press, 2001. [PDF]
• D. Blei and L. Kaelbling. Shortest paths in a dynamic uncertain domain. In IJCAI Workshop on Adaptive Spatial Representations of Dynamic Environments, 1999. [PDF]